cs.AI updates on arXiv.org 07月08日 12:34
Preference-Optimal Multi-Metric Weighting for Parallel Coordinate Plots
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本文提出一种基于最优权重的PCP多指标解释方法,通过雷达图可视化技术帮助用户选择偏好,并在行人流量引导规划中验证了方法的有效性。

arXiv:2507.02905v1 Announce Type: cross Abstract: Parallel coordinate plots (PCPs) are a prevalent method to interpret the relationship between the control parameters and metrics. PCPs deliver such an interpretation by color gradation based on a single metric. However, it is challenging to provide such a gradation when multiple metrics are present. Although a naive approach involves calculating a single metric by linearly weighting each metric, such weighting is unclear for users. To address this problem, we first propose a principled formulation for calculating the optimal weight based on a specific preferred metric combination. Although users can simply select their preference from a two-dimensional (2D) plane for bi-metric problems, multi-metric problems require intuitive visualization to allow them to select their preference. We achieved this using various radar charts to visualize the metric trade-offs on the 2D plane reduced by UMAP. In the analysis using pedestrian flow guidance planning, our method identified unique patterns of control parameter importance for each user preference, highlighting the effectiveness of our method.

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相关标签

PCP 多指标权重 可视化 行人流量规划 雷达图
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